Overview

Dataset statistics

Number of variables14
Number of observations3687
Missing cells9680
Missing cells (%)18.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory403.4 KiB
Average record size in memory112.0 B

Variable types

Numeric7
Text2
Boolean2
Categorical3

Alerts

add_certificate has constant value ""Constant
Id is highly overall correlated with price and 5 other fieldsHigh correlation
price is highly overall correlated with Id and 2 other fieldsHigh correlation
num_subscribers is highly overall correlated with Id and 3 other fieldsHigh correlation
content_duration is highly overall correlated with Id and 4 other fieldsHigh correlation
ingreso_por_curso is highly overall correlated with Id and 3 other fieldsHigh correlation
price_Certificate is highly overall correlated with ingreso_por_curso and 2 other fieldsHigh correlation
is_paid is highly overall correlated with Id and 5 other fieldsHigh correlation
plataforma is highly overall correlated with Id and 4 other fieldsHigh correlation
language is highly imbalanced (80.3%)Imbalance
is_paid has 491 (13.3%) missing valuesMissing
price has 491 (13.3%) missing valuesMissing
level has 287 (7.8%) missing valuesMissing
content_duration has 491 (13.3%) missing valuesMissing
rating has 867 (23.5%) missing valuesMissing
language has 448 (12.2%) missing valuesMissing
category has 448 (12.2%) missing valuesMissing
ingreso_por_curso has 491 (13.3%) missing valuesMissing
add_certificate has 2833 (76.8%) missing valuesMissing
price_Certificate has 2833 (76.8%) missing valuesMissing
num_subscribers is highly skewed (γ1 = 38.93832754)Skewed
Id is uniformly distributedUniform
Id has unique valuesUnique
price has 1070 (29.0%) zerosZeros
ingreso_por_curso has 236 (6.4%) zerosZeros

Reproduction

Analysis started2023-06-17 01:48:02.861906
Analysis finished2023-06-17 01:48:17.023082
Duration14.16 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

Id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct3687
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1844
Minimum1
Maximum3687
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2023-06-16T22:48:17.167948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile185.3
Q1922.5
median1844
Q32765.5
95-th percentile3502.7
Maximum3687
Range3686
Interquartile range (IQR)1843

Descriptive statistics

Standard deviation1064.4895
Coefficient of variation (CV)0.57727199
Kurtosis-1.2
Mean1844
Median Absolute Deviation (MAD)922
Skewness0
Sum6798828
Variance1133138
MonotonicityStrictly increasing
2023-06-16T22:48:17.399235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2450 1
 
< 0.1%
2452 1
 
< 0.1%
2453 1
 
< 0.1%
2454 1
 
< 0.1%
2455 1
 
< 0.1%
2456 1
 
< 0.1%
2457 1
 
< 0.1%
2458 1
 
< 0.1%
2459 1
 
< 0.1%
Other values (3677) 3677
99.7%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
3687 1
< 0.1%
3686 1
< 0.1%
3685 1
< 0.1%
3684 1
< 0.1%
3683 1
< 0.1%
3682 1
< 0.1%
3681 1
< 0.1%
3680 1
< 0.1%
3679 1
< 0.1%
3678 1
< 0.1%
Distinct3652
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
2023-06-16T22:48:17.825888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length243
Median length84
Mean length43.252509
Min length7

Characters and Unicode

Total characters159472
Distinct characters466
Distinct categories16 ?
Distinct scripts8 ?
Distinct blocks12 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3624 ?
Unique (%)98.3%

Sample

1st rowUltimate Investment Banking Course
2nd rowComplete GST Course & Certification - Grow Your CA Practice
3rd rowFinancial Modeling for Business Analysts and Consultants
4th rowBeginner to Pro - Financial Analysis in Excel 2017
5th rowHow To Maximize Your Profits Trading Options
ValueCountFrequency (%)
786
 
3.3%
to 669
 
2.8%
and 661
 
2.8%
for 543
 
2.3%
the 494
 
2.1%
a 362
 
1.5%
learn 327
 
1.4%
in 325
 
1.4%
with 298
 
1.2%
of 228
 
1.0%
Other values (4377) 19202
80.4%
2023-06-16T22:48:18.682552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20290
 
12.7%
e 12782
 
8.0%
n 10735
 
6.7%
i 9981
 
6.3%
a 9922
 
6.2%
o 9678
 
6.1%
t 8980
 
5.6%
r 8768
 
5.5%
s 7387
 
4.6%
l 4683
 
2.9%
Other values (456) 56266
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 113753
71.3%
Space Separator 20309
 
12.7%
Uppercase Letter 19839
 
12.4%
Other Punctuation 2133
 
1.3%
Decimal Number 1394
 
0.9%
Other Letter 983
 
0.6%
Dash Punctuation 675
 
0.4%
Close Punctuation 120
 
0.1%
Open Punctuation 120
 
0.1%
Math Symbol 75
 
< 0.1%
Other values (6) 71
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ل 46
 
4.7%
ا 41
 
4.2%
و 24
 
2.4%
ي 19
 
1.9%
19
 
1.9%
19
 
1.9%
م 19
 
1.9%
17
 
1.7%
17
 
1.7%
ر 16
 
1.6%
Other values (281) 746
75.9%
Lowercase Letter
ValueCountFrequency (%)
e 12782
11.2%
n 10735
9.4%
i 9981
 
8.8%
a 9922
 
8.7%
o 9678
 
8.5%
t 8980
 
7.9%
r 8768
 
7.7%
s 7387
 
6.5%
l 4683
 
4.1%
c 4401
 
3.9%
Other values (61) 26436
23.2%
Uppercase Letter
ValueCountFrequency (%)
S 1877
 
9.5%
C 1741
 
8.8%
P 1680
 
8.5%
T 1460
 
7.4%
A 1332
 
6.7%
B 1232
 
6.2%
M 1120
 
5.6%
F 1037
 
5.2%
L 1037
 
5.2%
D 1016
 
5.1%
Other values (27) 6307
31.8%
Other Punctuation
ValueCountFrequency (%)
: 989
46.4%
, 340
 
15.9%
& 230
 
10.8%
. 167
 
7.8%
! 146
 
6.8%
' 108
 
5.1%
/ 61
 
2.9%
# 25
 
1.2%
" 20
 
0.9%
? 16
 
0.8%
Other values (8) 31
 
1.5%
Decimal Number
ValueCountFrequency (%)
1 363
26.0%
2 238
17.1%
0 222
15.9%
5 150
10.8%
3 136
 
9.8%
4 98
 
7.0%
6 60
 
4.3%
7 55
 
3.9%
8 36
 
2.6%
9 31
 
2.2%
Other values (3) 5
 
0.4%
Math Symbol
ValueCountFrequency (%)
+ 36
48.0%
| 33
44.0%
> 2
 
2.7%
1
 
1.3%
1
 
1.3%
1
 
1.3%
= 1
 
1.3%
Close Punctuation
ValueCountFrequency (%)
) 109
90.8%
3
 
2.5%
3
 
2.5%
] 2
 
1.7%
2
 
1.7%
} 1
 
0.8%
Open Punctuation
ValueCountFrequency (%)
( 109
90.8%
3
 
2.5%
3
 
2.5%
[ 2
 
1.7%
2
 
1.7%
{ 1
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 648
96.0%
24
 
3.6%
2
 
0.3%
1
 
0.1%
Space Separator
ValueCountFrequency (%)
20290
99.9%
  12
 
0.1%
  7
 
< 0.1%
Letter Number
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Other Symbol
ValueCountFrequency (%)
® 12
85.7%
2
 
14.3%
Modifier Symbol
ValueCountFrequency (%)
` 1
50.0%
´ 1
50.0%
Final Punctuation
ValueCountFrequency (%)
24
100.0%
Modifier Letter
ValueCountFrequency (%)
23
100.0%
Control
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 133378
83.6%
Common 24893
 
15.6%
Han 318
 
0.2%
Arabic 285
 
0.2%
Cyrillic 218
 
0.1%
Katakana 192
 
0.1%
Hiragana 179
 
0.1%
Hangul 9
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
14
 
4.4%
7
 
2.2%
7
 
2.2%
6
 
1.9%
6
 
1.9%
5
 
1.6%
5
 
1.6%
5
 
1.6%
5
 
1.6%
5
 
1.6%
Other values (156) 253
79.6%
Latin
ValueCountFrequency (%)
e 12782
 
9.6%
n 10735
 
8.0%
i 9981
 
7.5%
a 9922
 
7.4%
o 9678
 
7.3%
t 8980
 
6.7%
r 8768
 
6.6%
s 7387
 
5.5%
l 4683
 
3.5%
c 4401
 
3.3%
Other values (68) 46061
34.5%
Common
ValueCountFrequency (%)
20290
81.5%
: 989
 
4.0%
- 648
 
2.6%
1 363
 
1.5%
, 340
 
1.4%
2 238
 
1.0%
& 230
 
0.9%
0 222
 
0.9%
. 167
 
0.7%
5 150
 
0.6%
Other values (54) 1256
 
5.0%
Katakana
ValueCountFrequency (%)
19
 
9.9%
17
 
8.9%
14
 
7.3%
12
 
6.2%
10
 
5.2%
10
 
5.2%
8
 
4.2%
7
 
3.6%
7
 
3.6%
6
 
3.1%
Other values (36) 82
42.7%
Hiragana
ValueCountFrequency (%)
19
 
10.6%
17
 
9.5%
15
 
8.4%
12
 
6.7%
11
 
6.1%
7
 
3.9%
7
 
3.9%
7
 
3.9%
5
 
2.8%
5
 
2.8%
Other values (32) 74
41.3%
Cyrillic
ValueCountFrequency (%)
а 28
12.8%
н 20
 
9.2%
о 19
 
8.7%
и 19
 
8.7%
р 14
 
6.4%
т 13
 
6.0%
е 12
 
5.5%
в 10
 
4.6%
л 10
 
4.6%
м 8
 
3.7%
Other values (23) 65
29.8%
Arabic
ValueCountFrequency (%)
ل 46
16.1%
ا 41
14.4%
و 24
 
8.4%
ي 19
 
6.7%
م 19
 
6.7%
ر 16
 
5.6%
ت 14
 
4.9%
س 14
 
4.9%
د 10
 
3.5%
ة 10
 
3.5%
Other values (18) 72
25.3%
Hangul
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 157736
98.9%
None 455
 
0.3%
CJK 318
 
0.2%
Arabic 285
 
0.2%
Cyrillic 218
 
0.1%
Katakana 215
 
0.1%
Hiragana 179
 
0.1%
Punctuation 49
 
< 0.1%
Hangul 9
 
< 0.1%
Number Forms 4
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20290
 
12.9%
e 12782
 
8.1%
n 10735
 
6.8%
i 9981
 
6.3%
a 9922
 
6.3%
o 9678
 
6.1%
t 8980
 
5.7%
r 8768
 
5.6%
s 7387
 
4.7%
l 4683
 
3.0%
Other values (79) 54530
34.6%
None
ValueCountFrequency (%)
ó 141
31.0%
á 51
 
11.2%
í 49
 
10.8%
é 38
 
8.4%
ñ 24
 
5.3%
ã 16
 
3.5%
ç 15
 
3.3%
  12
 
2.6%
® 12
 
2.6%
ú 12
 
2.6%
Other values (33) 85
18.7%
Arabic
ValueCountFrequency (%)
ل 46
16.1%
ا 41
14.4%
و 24
 
8.4%
ي 19
 
6.7%
م 19
 
6.7%
ر 16
 
5.6%
ت 14
 
4.9%
س 14
 
4.9%
د 10
 
3.5%
ة 10
 
3.5%
Other values (18) 72
25.3%
Cyrillic
ValueCountFrequency (%)
а 28
12.8%
н 20
 
9.2%
о 19
 
8.7%
и 19
 
8.7%
р 14
 
6.4%
т 13
 
6.0%
е 12
 
5.5%
в 10
 
4.6%
л 10
 
4.6%
м 8
 
3.7%
Other values (23) 65
29.8%
Punctuation
ValueCountFrequency (%)
24
49.0%
24
49.0%
1
 
2.0%
Katakana
ValueCountFrequency (%)
23
 
10.7%
19
 
8.8%
17
 
7.9%
14
 
6.5%
12
 
5.6%
10
 
4.7%
10
 
4.7%
8
 
3.7%
7
 
3.3%
7
 
3.3%
Other values (37) 88
40.9%
Hiragana
ValueCountFrequency (%)
19
 
10.6%
17
 
9.5%
15
 
8.4%
12
 
6.7%
11
 
6.1%
7
 
3.9%
7
 
3.9%
7
 
3.9%
5
 
2.8%
5
 
2.8%
Other values (32) 74
41.3%
CJK
ValueCountFrequency (%)
14
 
4.4%
7
 
2.2%
7
 
2.2%
6
 
1.9%
6
 
1.9%
5
 
1.6%
5
 
1.6%
5
 
1.6%
5
 
1.6%
5
 
1.6%
Other values (156) 253
79.6%
Letterlike Symbols
ValueCountFrequency (%)
2
100.0%
Number Forms
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Math Operators
ValueCountFrequency (%)
1
50.0%
1
50.0%
Hangul
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%

is_paid
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.1%
Missing491
Missing (%)13.3%
Memory size28.9 KiB
True
2126 
False
1070 
(Missing)
491 
ValueCountFrequency (%)
True 2126
57.7%
False 1070
29.0%
(Missing) 491
 
13.3%
2023-06-16T22:48:18.906567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

price
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct38
Distinct (%)1.2%
Missing491
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean53.110138
Minimum0
Maximum200
Zeros1070
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2023-06-16T22:48:19.081967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median30
Q380
95-th percentile200
Maximum200
Range200
Interquartile range (IQR)80

Descriptive statistics

Standard deviation63.108782
Coefficient of variation (CV)1.1882624
Kurtosis0.37585896
Mean53.110138
Median Absolute Deviation (MAD)30
Skewness1.2496335
Sum169740
Variance3982.7184
MonotonicityNot monotonic
2023-06-16T22:48:19.310235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 1070
29.0%
20 396
 
10.7%
50 256
 
6.9%
200 212
 
5.7%
40 148
 
4.0%
30 113
 
3.1%
95 105
 
2.8%
100 105
 
2.8%
195 101
 
2.7%
25 86
 
2.3%
Other values (28) 604
16.4%
(Missing) 491
13.3%
ValueCountFrequency (%)
0 1070
29.0%
20 396
 
10.7%
25 86
 
2.3%
30 113
 
3.1%
35 67
 
1.8%
40 148
 
4.0%
45 64
 
1.7%
50 256
 
6.9%
55 24
 
0.7%
60 62
 
1.7%
ValueCountFrequency (%)
200 212
5.7%
195 101
2.7%
190 8
 
0.2%
185 5
 
0.1%
180 14
 
0.4%
175 10
 
0.3%
170 2
 
0.1%
165 5
 
0.1%
160 3
 
0.1%
155 1
 
< 0.1%

num_subscribers
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2630
Distinct (%)71.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46260.841
Minimum0
Maximum32000000
Zeros20
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2023-06-16T22:48:19.546311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23
Q1652.5
median4005
Q322984
95-th percentile120013.3
Maximum32000000
Range32000000
Interquartile range (IQR)22331.5

Descriptive statistics

Standard deviation705431.65
Coefficient of variation (CV)15.249002
Kurtosis1597.3903
Mean46260.841
Median Absolute Deviation (MAD)3947
Skewness38.938328
Sum1.7056372 × 108
Variance4.9763381 × 1011
MonotonicityNot monotonic
2023-06-16T22:48:19.774221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
 
0.5%
120000 15
 
0.4%
130000 13
 
0.4%
7 13
 
0.4%
1 13
 
0.4%
11 12
 
0.3%
2 12
 
0.3%
12000 11
 
0.3%
110000 11
 
0.3%
5 10
 
0.3%
Other values (2620) 3557
96.5%
ValueCountFrequency (%)
0 20
0.5%
1 13
0.4%
2 12
0.3%
3 7
 
0.2%
4 7
 
0.2%
5 10
0.3%
6 5
 
0.1%
7 13
0.4%
8 6
 
0.2%
9 10
0.3%
ValueCountFrequency (%)
32000000 1
< 0.1%
25000000 1
< 0.1%
13000000 1
< 0.1%
2442271 1
< 0.1%
1103777 1
< 0.1%
1022489 1
< 0.1%
760000 1
< 0.1%
750000 1
< 0.1%
698950 1
< 0.1%
642088 1
< 0.1%

level
Categorical

Distinct4
Distinct (%)0.1%
Missing287
Missing (%)7.8%
Memory size28.9 KiB
Beginner
1460 
All
1291 
Intermediate
529 
Expert
 
120

Length

Max length12
Median length8
Mean length6.6532353
Min length3

Characters and Unicode

Total characters22621
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll
2nd rowAll
3rd rowIntermediate
4th rowAll
5th rowIntermediate

Common Values

ValueCountFrequency (%)
Beginner 1460
39.6%
All 1291
35.0%
Intermediate 529
 
14.3%
Expert 120
 
3.3%
(Missing) 287
 
7.8%

Length

2023-06-16T22:48:19.994177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-16T22:48:20.195420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
beginner 1460
42.9%
all 1291
38.0%
intermediate 529
 
15.6%
expert 120
 
3.5%

Most occurring characters

ValueCountFrequency (%)
e 4627
20.5%
n 3449
15.2%
l 2582
11.4%
r 2109
9.3%
i 1989
8.8%
B 1460
 
6.5%
g 1460
 
6.5%
A 1291
 
5.7%
t 1178
 
5.2%
I 529
 
2.3%
Other values (6) 1947
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19221
85.0%
Uppercase Letter 3400
 
15.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4627
24.1%
n 3449
17.9%
l 2582
13.4%
r 2109
11.0%
i 1989
10.3%
g 1460
 
7.6%
t 1178
 
6.1%
m 529
 
2.8%
d 529
 
2.8%
a 529
 
2.8%
Other values (2) 240
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
B 1460
42.9%
A 1291
38.0%
I 529
 
15.6%
E 120
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 22621
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4627
20.5%
n 3449
15.2%
l 2582
11.4%
r 2109
9.3%
i 1989
8.8%
B 1460
 
6.5%
g 1460
 
6.5%
A 1291
 
5.7%
t 1178
 
5.2%
I 529
 
2.3%
Other values (6) 1947
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22621
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4627
20.5%
n 3449
15.2%
l 2582
11.4%
r 2109
9.3%
i 1989
8.8%
B 1460
 
6.5%
g 1460
 
6.5%
A 1291
 
5.7%
t 1178
 
5.2%
I 529
 
2.3%
Other values (6) 1947
8.6%

content_duration
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct115
Distinct (%)3.6%
Missing491
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean310.8531
Minimum0.31666667
Maximum3024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2023-06-16T22:48:20.401997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.31666667
5-th percentile1
Q11.5
median4
Q3672
95-th percentile1680
Maximum3024
Range3023.6833
Interquartile range (IQR)670.5

Descriptive statistics

Standard deviation576.2475
Coefficient of variation (CV)1.8537614
Kurtosis3.2497266
Mean310.8531
Median Absolute Deviation (MAD)3
Skewness1.9340923
Sum993486.52
Variance332061.18
MonotonicityNot monotonic
2023-06-16T22:48:20.617321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5 331
 
9.0%
1 325
 
8.8%
2 259
 
7.0%
2.5 180
 
4.9%
1008 173
 
4.7%
3 162
 
4.4%
672 159
 
4.3%
3.5 121
 
3.3%
840 119
 
3.2%
4 98
 
2.7%
Other values (105) 1269
34.4%
(Missing) 491
 
13.3%
ValueCountFrequency (%)
0.3166666667 1
 
< 0.1%
0.4666666667 1
 
< 0.1%
0.5 9
0.2%
0.5166666667 6
 
0.2%
0.5333333333 13
0.4%
0.55 8
0.2%
0.5666666667 11
0.3%
0.5833333333 6
 
0.2%
0.6 8
0.2%
0.6166666667 16
0.4%
ValueCountFrequency (%)
3024 3
 
0.1%
2856 1
 
< 0.1%
2688 12
 
0.3%
2520 18
 
0.5%
2352 15
 
0.4%
2184 6
 
0.2%
2016 34
0.9%
1848 6
 
0.2%
1680 79
2.1%
1512 20
 
0.5%

rating
Real number (ℝ)

Distinct30
Distinct (%)1.1%
Missing867
Missing (%)23.5%
Infinite0
Infinite (%)0.0%
Mean4.2868794
Minimum0
Maximum5
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2023-06-16T22:48:20.824830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.4
Q14
median4.4
Q34.6
95-th percentile4.8
Maximum5
Range5
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.495934
Coefficient of variation (CV)0.11568648
Kurtosis11.535028
Mean4.2868794
Median Absolute Deviation (MAD)0.3
Skewness-2.2072273
Sum12089
Variance0.24595053
MonotonicityNot monotonic
2023-06-16T22:48:21.025643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4.4 376
10.2%
4.6 341
 
9.2%
4.8 276
 
7.5%
4.2 272
 
7.4%
4.7 215
 
5.8%
4 201
 
5.5%
4.5 200
 
5.4%
4.3 163
 
4.4%
3.8 143
 
3.9%
4.1 104
 
2.8%
Other values (20) 529
14.3%
(Missing) 867
23.5%
ValueCountFrequency (%)
0 4
 
0.1%
1 5
 
0.1%
2 4
 
0.1%
2.2 3
 
0.1%
2.5 3
 
0.1%
2.6 4
 
0.1%
2.7 1
 
< 0.1%
2.8 11
0.3%
2.9 1
 
< 0.1%
3 18
0.5%
ValueCountFrequency (%)
5 37
 
1.0%
4.9 99
 
2.7%
4.8 276
7.5%
4.7 215
5.8%
4.6 341
9.2%
4.5 200
5.4%
4.4 376
10.2%
4.3 163
4.4%
4.2 272
7.4%
4.1 104
 
2.8%

language
Categorical

IMBALANCE  MISSING 

Distinct14
Distinct (%)0.4%
Missing448
Missing (%)12.2%
Memory size28.9 KiB
English
2872 
Spanish
 
220
Portuguese
 
46
Japanese
 
29
German
 
19
Other values (9)
 
53

Length

Max length10
Median length7
Mean length7.0342698
Min length4

Characters and Unicode

Total characters22784
Distinct characters32
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowEnglish
2nd rowEnglish
3rd rowEnglish
4th rowEnglish
5th rowEnglish

Common Values

ValueCountFrequency (%)
English 2872
77.9%
Spanish 220
 
6.0%
Portuguese 46
 
1.2%
Japanese 29
 
0.8%
German 19
 
0.5%
French 17
 
0.5%
Arabic 12
 
0.3%
Italian 8
 
0.2%
Russian 4
 
0.1%
Chinese 4
 
0.1%
Other values (4) 8
 
0.2%
(Missing) 448
 
12.2%

Length

2023-06-16T22:48:21.231725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english 2872
88.7%
spanish 220
 
6.8%
portuguese 46
 
1.4%
japanese 29
 
0.9%
german 19
 
0.6%
french 17
 
0.5%
arabic 12
 
0.4%
italian 8
 
0.2%
russian 4
 
0.1%
chinese 4
 
0.1%
Other values (4) 8
 
0.2%

Most occurring characters

ValueCountFrequency (%)
s 3183
14.0%
n 3176
13.9%
i 3128
13.7%
h 3117
13.7%
g 2918
12.8%
l 2880
12.6%
E 2872
12.6%
a 330
 
1.4%
p 249
 
1.1%
S 220
 
1.0%
Other values (22) 711
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19545
85.8%
Uppercase Letter 3239
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 3183
16.3%
n 3176
16.2%
i 3128
16.0%
h 3117
15.9%
g 2918
14.9%
l 2880
14.7%
a 330
 
1.7%
p 249
 
1.3%
e 195
 
1.0%
u 101
 
0.5%
Other values (8) 268
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
E 2872
88.7%
S 220
 
6.8%
P 46
 
1.4%
J 29
 
0.9%
G 19
 
0.6%
F 17
 
0.5%
A 12
 
0.4%
I 8
 
0.2%
R 4
 
0.1%
C 4
 
0.1%
Other values (4) 8
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 22784
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 3183
14.0%
n 3176
13.9%
i 3128
13.7%
h 3117
13.7%
g 2918
12.8%
l 2880
12.6%
E 2872
12.6%
a 330
 
1.4%
p 249
 
1.1%
S 220
 
1.0%
Other values (22) 711
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 3183
14.0%
n 3176
13.9%
i 3128
13.7%
h 3117
13.7%
g 2918
12.8%
l 2880
12.6%
E 2872
12.6%
a 330
 
1.4%
p 249
 
1.1%
S 220
 
1.0%
Other values (22) 711
 
3.1%
Distinct54
Distinct (%)1.7%
Missing448
Missing (%)12.2%
Memory size28.9 KiB
2023-06-16T22:48:21.485273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length28
Median length26
Mean length13.103736
Min length3

Characters and Unicode

Total characters42443
Distinct characters42
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.2%

Sample

1st rowFinance & Accounting
2nd rowFinance & Accounting
3rd rowFinance & Accounting
4th rowFinance & Accounting
5th rowFinance & Accounting
ValueCountFrequency (%)
1070
18.5%
development 852
14.8%
finance 763
13.2%
accounting 726
12.6%
music 407
 
7.1%
design 363
 
6.3%
science 159
 
2.8%
business 156
 
2.7%
computer 150
 
2.6%
management 149
 
2.6%
Other values (63) 977
16.9%
2023-06-16T22:48:21.962246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 5500
13.0%
e 5234
12.3%
i 3561
 
8.4%
c 3469
 
8.2%
2533
 
6.0%
t 2446
 
5.8%
o 2133
 
5.0%
s 1814
 
4.3%
a 1706
 
4.0%
u 1647
 
3.9%
Other values (32) 12400
29.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34153
80.5%
Uppercase Letter 4687
 
11.0%
Space Separator 2533
 
6.0%
Other Punctuation 1070
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 5500
16.1%
e 5234
15.3%
i 3561
10.4%
c 3469
10.2%
t 2446
7.2%
o 2133
 
6.2%
s 1814
 
5.3%
a 1706
 
5.0%
u 1647
 
4.8%
g 1467
 
4.3%
Other values (12) 5176
15.2%
Uppercase Letter
ValueCountFrequency (%)
D 1279
27.3%
A 813
17.3%
F 769
16.4%
M 609
13.0%
S 381
 
8.1%
C 202
 
4.3%
B 190
 
4.1%
E 159
 
3.4%
H 88
 
1.9%
L 81
 
1.7%
Other values (8) 116
 
2.5%
Space Separator
ValueCountFrequency (%)
2533
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1070
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38840
91.5%
Common 3603
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 5500
14.2%
e 5234
13.5%
i 3561
 
9.2%
c 3469
 
8.9%
t 2446
 
6.3%
o 2133
 
5.5%
s 1814
 
4.7%
a 1706
 
4.4%
u 1647
 
4.2%
g 1467
 
3.8%
Other values (30) 9863
25.4%
Common
ValueCountFrequency (%)
2533
70.3%
& 1070
29.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42443
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 5500
13.0%
e 5234
12.3%
i 3561
 
8.4%
c 3469
 
8.2%
2533
 
6.0%
t 2446
 
5.8%
o 2133
 
5.0%
s 1814
 
4.3%
a 1706
 
4.0%
u 1647
 
3.9%
Other values (32) 12400
29.2%

ingreso_por_curso
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct2664
Distinct (%)83.4%
Missing491
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean1783000.8
Minimum0
Maximum2.1980439 × 108
Zeros236
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2023-06-16T22:48:22.200296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19642.5
median108835
Q3882197.75
95-th percentile7941362
Maximum2.1980439 × 108
Range2.1980439 × 108
Interquartile range (IQR)872555.25

Descriptive statistics

Standard deviation7425632.3
Coefficient of variation (CV)4.1646825
Kurtosis298.61903
Mean1783000.8
Median Absolute Deviation (MAD)108775
Skewness13.880577
Sum5.6984706 × 109
Variance5.5140014 × 1013
MonotonicityNot monotonic
2023-06-16T22:48:22.428580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 236
 
6.4%
200 8
 
0.2%
900 7
 
0.2%
760 5
 
0.1%
60 5
 
0.1%
1000 5
 
0.1%
540 5
 
0.1%
350 5
 
0.1%
600 5
 
0.1%
1500 5
 
0.1%
Other values (2654) 2910
78.9%
(Missing) 491
 
13.3%
ValueCountFrequency (%)
0 236
6.4%
20 2
 
0.1%
30 4
 
0.1%
35 1
 
< 0.1%
40 3
 
0.1%
50 2
 
0.1%
60 5
 
0.1%
65 1
 
< 0.1%
70 2
 
0.1%
75 1
 
< 0.1%
ValueCountFrequency (%)
219804390 1
< 0.1%
127775512 1
< 0.1%
109273923 1
< 0.1%
93442300 1
< 0.1%
80378766 1
< 0.1%
78331914 1
< 0.1%
66843105 1
< 0.1%
66010896 1
< 0.1%
62905500 1
< 0.1%
61684500 1
< 0.1%

plataforma
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
Udemy
2342 
edX
854 
Coursera
491 

Length

Max length8
Median length5
Mean length4.9362625
Min length3

Characters and Unicode

Total characters18200
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUdemy
2nd rowUdemy
3rd rowUdemy
4th rowUdemy
5th rowUdemy

Common Values

ValueCountFrequency (%)
Udemy 2342
63.5%
edX 854
 
23.2%
Coursera 491
 
13.3%

Length

2023-06-16T22:48:22.631869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-16T22:48:22.831682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
udemy 2342
63.5%
edx 854
 
23.2%
coursera 491
 
13.3%

Most occurring characters

ValueCountFrequency (%)
e 3687
20.3%
d 3196
17.6%
U 2342
12.9%
m 2342
12.9%
y 2342
12.9%
r 982
 
5.4%
X 854
 
4.7%
C 491
 
2.7%
o 491
 
2.7%
u 491
 
2.7%
Other values (2) 982
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14513
79.7%
Uppercase Letter 3687
 
20.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3687
25.4%
d 3196
22.0%
m 2342
16.1%
y 2342
16.1%
r 982
 
6.8%
o 491
 
3.4%
u 491
 
3.4%
s 491
 
3.4%
a 491
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
U 2342
63.5%
X 854
 
23.2%
C 491
 
13.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 18200
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3687
20.3%
d 3196
17.6%
U 2342
12.9%
m 2342
12.9%
y 2342
12.9%
r 982
 
5.4%
X 854
 
4.7%
C 491
 
2.7%
o 491
 
2.7%
u 491
 
2.7%
Other values (2) 982
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3687
20.3%
d 3196
17.6%
U 2342
12.9%
m 2342
12.9%
y 2342
12.9%
r 982
 
5.4%
X 854
 
4.7%
C 491
 
2.7%
o 491
 
2.7%
u 491
 
2.7%
Other values (2) 982
 
5.4%

add_certificate
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing2833
Missing (%)76.8%
Memory size28.9 KiB
True
854 
(Missing)
2833 
ValueCountFrequency (%)
True 854
 
23.2%
(Missing) 2833
76.8%
2023-06-16T22:48:23.019421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

price_Certificate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)4.8%
Missing2833
Missing (%)76.8%
Infinite0
Infinite (%)0.0%
Mean101.64286
Minimum5
Maximum450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2023-06-16T22:48:23.196741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile25
Q149
median79
Q3149
95-th percentile249
Maximum450
Range445
Interquartile range (IQR)100

Descriptive statistics

Standard deviation70.640447
Coefficient of variation (CV)0.69498683
Kurtosis1.8183625
Mean101.64286
Median Absolute Deviation (MAD)30
Skewness1.2949606
Sum86803
Variance4990.0728
MonotonicityNot monotonic
2023-06-16T22:48:23.403713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
49 240
 
6.5%
99 126
 
3.4%
50 83
 
2.3%
199 73
 
2.0%
149 62
 
1.7%
25 43
 
1.2%
139 31
 
0.8%
150 29
 
0.8%
249 25
 
0.7%
169 19
 
0.5%
Other values (31) 123
 
3.3%
(Missing) 2833
76.8%
ValueCountFrequency (%)
5 7
 
0.2%
10 1
 
< 0.1%
15 1
 
< 0.1%
19 2
 
0.1%
25 43
 
1.2%
29 11
 
0.3%
39 9
 
0.2%
40 2
 
0.1%
49 240
6.5%
50 83
 
2.3%
ValueCountFrequency (%)
450 1
 
< 0.1%
399 3
 
0.1%
375 1
 
< 0.1%
350 3
 
0.1%
300 3
 
0.1%
299 10
 
0.3%
250 2
 
0.1%
249 25
0.7%
225 3
 
0.1%
214 3
 
0.1%

Interactions

2023-06-16T22:48:14.592767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:04.234810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:05.679393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:09.100704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:10.564704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:11.936787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:13.268428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:14.777599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:04.452609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:05.877210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:09.391706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:10.760493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:12.127609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:13.463253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:14.956702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:04.659417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:06.092012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:09.601514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:10.966220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:12.345405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:13.668628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:15.144529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:04.876216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:06.291757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:09.796330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:11.162400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:12.538295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:13.854455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:15.343346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:05.103006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:06.494566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:09.997307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:11.362717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:12.747100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:14.045281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:15.493205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:05.305818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:06.687725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:10.190721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:11.558539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:12.926861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:14.228107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:15.687027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:05.492566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:06.893533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:10.375001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:11.746961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:13.117568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T22:48:14.412935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-16T22:48:23.580481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Idpricenum_subscriberscontent_durationratingingreso_por_cursoprice_Certificateis_paidlevellanguageplataforma
Id1.000-0.5550.7300.6440.2890.5820.0000.7950.2660.1300.894
price-0.5551.000-0.486-0.3910.016-0.139NaN0.7690.2240.0710.654
num_subscribers0.730-0.4861.0000.6100.2640.8020.1381.0000.0000.0000.043
content_duration0.644-0.3910.6101.0000.1330.6820.0940.8450.3010.1260.993
rating0.2890.0160.2640.1331.000-0.015NaN0.0870.0780.2620.421
ingreso_por_curso0.582-0.1390.8020.682-0.0151.0000.6250.1510.0740.0000.181
price_Certificate0.000NaN0.1380.094NaN0.6251.0001.0000.3290.1281.000
is_paid0.7950.7691.0000.8450.0870.1511.0001.0000.4520.2230.850
level0.2660.2240.0000.3010.0780.0740.3290.4521.0000.0940.339
language0.1300.0710.0000.1260.2620.0000.1280.2230.0941.0000.172
plataforma0.8940.6540.0430.9930.4210.1811.0000.8500.3390.1721.000

Missing values

2023-06-16T22:48:15.968765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-16T22:48:16.431333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-16T22:48:16.777383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Idcourse_titleis_paidpricenum_subscriberslevelcontent_durationratinglanguagecategoryingreso_por_cursoplataformaadd_certificateprice_Certificate
01Ultimate Investment Banking CourseTrue200.02147.0All1.53.6EnglishFinance & Accounting429400.0UdemyNaNNaN
12Complete GST Course & Certification - Grow Your CA PracticeTrue75.02792.0All39.04.4EnglishFinance & Accounting209400.0UdemyNaNNaN
23Financial Modeling for Business Analysts and ConsultantsTrue45.02174.0Intermediate2.54.4EnglishFinance & Accounting97830.0UdemyNaNNaN
34Beginner to Pro - Financial Analysis in Excel 2017True95.02451.0All3.04.2EnglishFinance & Accounting232845.0UdemyNaNNaN
45How To Maximize Your Profits Trading OptionsTrue200.01276.0Intermediate2.04.7EnglishFinance & Accounting255200.0UdemyNaNNaN
56Investing And Trading For Beginners: Mastering Price ChartsTrue65.01540.0Beginner1.04.6EnglishFinance & Accounting100100.0UdemyNaNNaN
67Trading Stock Chart Patterns For Immediate, Explosive GainsTrue95.02917.0All2.54.2EnglishFinance & Accounting277115.0UdemyNaNNaN
78The Only Investment Strategy You Need For Your RetirementTrue200.0827.0All1.03.9EnglishFinance & Accounting165400.0UdemyNaNNaN
89Forex Trading Secrets of the Pros With Amazon's AWSTrue200.04284.0All5.03.8EnglishFinance & Accounting856800.0UdemyNaNNaN
910Trading Options With Money FlowTrue200.01380.0All1.04.3EnglishFinance & Accounting276000.0UdemyNaNNaN
Idcourse_titleis_paidpricenum_subscriberslevelcontent_durationratinglanguagecategoryingreso_por_cursoplataformaadd_certificateprice_Certificate
36773678Mastering Final Cut ProNaNNaN84000.0NaNNaN4.5NaNNaNNaNCourseraNaNNaN
36783679Tricky American English PronunciationNaNNaN110000.0NaNNaN4.7NaNNaNNaNCourseraNaNNaN
36793680International Law in Action: A Guide to the International Courts and Tribunals in The HagueNaNNaN36000.0AllNaN4.8NaNNaNNaNCourseraNaNNaN
36803681Geopolitics of EuropeNaNNaN19000.0AllNaN3.7NaNNaNNaNCourseraNaNNaN
36813682Osteoarchaeology: The Truth in Our BonesNaNNaN22000.0NaNNaN4.6NaNNaNNaNCourseraNaNNaN
36823683Mathematics for Machine Learning: PCANaNNaN33000.0NaNNaN3.4NaNNaNNaNCourseraNaNNaN
36833684Object Oriented Programming in JavaNaNNaN330000.0NaNNaN4.7NaNNaNNaNCourseraNaNNaN
36843685Математика и Python для анализа данныхNaNNaN67000.0NaNNaN4.6NaNNaNNaNCourseraNaNNaN
36853686Hacia una práctica constructivista en el aulaNaNNaN62000.0AllNaN4.9NaNNaNNaNCourseraNaNNaN
36863687A Crash Course in Data ScienceNaNNaN130000.0NaNNaNNaNNaNNaNNaNCourseraNaNNaN